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plots.py
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Mon, Feb 24, 01:31

plots.py
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# -*- coding: utf-8 -*-
"""function for plot."""
import matplotlib.pyplot as plt
import numpy as np
from grid_search import get_best_parameters
def prediction(w0, w1, mean_x, std_x):
"""Get the regression line from the model."""
x = np.arange(1.2, 2, 0.01)
x_normalized = (x - mean_x) / std_x
return x, w0 + w1 * x_normalized
def base_visualization(grid_losses, w0_list, w1_list,
mean_x, std_x, height, weight):
"""Base Visualization for both models."""
w0, w1 = np.meshgrid(w0_list, w1_list)
fig = plt.figure()
# plot contourf
ax1 = fig.add_subplot(1, 2, 1)
cp = ax1.contourf(w0, w1, grid_losses.T, cmap=plt.cm.jet)
fig.colorbar(cp, ax=ax1)
ax1.set_xlabel(r'$w_0$')
ax1.set_ylabel(r'$w_1$')
# put a marker at the minimum
loss_star, w0_star, w1_star = get_best_parameters(
w0_list, w1_list, grid_losses)
ax1.plot(w0_star, w1_star, marker='*', color='r', markersize=20)
# plot f(x)
ax2 = fig.add_subplot(1, 2, 2)
ax2.scatter(height, weight, marker=".", color='b', s=5)
ax2.set_xlabel("x")
ax2.set_ylabel("y")
ax2.grid()
return fig
def grid_visualization(grid_losses, w0_list, w1_list,
mean_x, std_x, height, weight):
"""Visualize how the trained model looks like under the grid search."""
fig = base_visualization(
grid_losses, w0_list, w1_list, mean_x, std_x, height, weight)
loss_star, w0_star, w1_star = get_best_parameters(
w0_list, w1_list, grid_losses)
# plot prediciton
x, f = prediction(w0_star, w1_star, mean_x, std_x)
ax2 = fig.get_axes()[2]
ax2.plot(x, f, 'r')
return fig
def gradient_descent_visualization(
gradient_losses, gradient_ws,
grid_losses, grid_w0, grid_w1,
mean_x, std_x, height, weight, n_iter=None):
"""Visualize how the loss value changes until n_iter."""
fig = base_visualization(
grid_losses, grid_w0, grid_w1, mean_x, std_x, height, weight)
ws_to_be_plotted = np.stack(gradient_ws)
if n_iter is not None:
ws_to_be_plotted = ws_to_be_plotted[:n_iter]
ax1, ax2 = fig.get_axes()[0], fig.get_axes()[2]
ax1.plot(
ws_to_be_plotted[:, 0], ws_to_be_plotted[:, 1],
marker='o', color='w', markersize=10)
pred_x, pred_y = prediction(
ws_to_be_plotted[-1, 0], ws_to_be_plotted[-1, 1],
mean_x, std_x)
ax2.plot(pred_x, pred_y, 'r')
return fig

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